Blog / code snippets and science rants

Over the past decade, comprehensive sequencing efforts have revealed the genomic landscapes of common forms of human cancer. In this review, Vogelstein at el. summarize what has been learned about cancer genomes from these sequencing studies and what this information has taught us about cancer biology and future cancer management strategies. Some key points of

Steps to create a Python executable for my-program.py. 1. You will need to create a file called my-executable (or whatever you want your executable’s name to be; so no extensions) with the following content: python my-program.py $* 2. Then you need to cd to your script’s folder and run the following command on the script

Python command for finding indices of differences between two strings. command: >>> [i for i in xrange(len(x)) if x[i] != y[i]] example: >>> x = ‘LEIYNQPNQEGPFDVQETEIAVQAKQPDVEEILSKGQHLYKEKPATQPVK’ >>> y = ‘LEIYNQPNQEGPFDVKETEIAVQAKQPDVEEILSKGQHLYKEKPATQPVK’ >>> [i for i in xrange(len(x)) if x[i] != y[i]] [15] This is particularly useful for finding position difference between two DNA or protein alignments

For nonsynonymous coding variants, functional impact prediction algorithms frequently make use of conservation of amino acid substitutions observed among homologous proteins at a given site under the assumption substitutions occurring at well-conserved sites will deleteriously impact protein function and substitutions occurring at less conserved sites will be tolerated. In this study, Marini et al. examine

Computational classifiers classify missense substitutions as pathogenetic or neutral based on inferences from evolutionary conservation using protein multiple sequence alignments (PMSAs) of the gene of interest (and other features). In this review, Tavtigian et al. make suggestions with respect to the important aspects of creating PMSAs that are informative for classification (and more). In using

Deep sequencing initiatives are identified a remarkable quantity of single nucleotide polymorphisms yet only approximately half are nonsynonymous amino acid substitutions that could potentially affect protein function. A confusing array of tools are available to help identify the few gene-coding variants that actually cause or confer susceptibility to disease. In this brief, Castellana and Mazza

G-protein-coupled receptors (GPCRs) play an important role in many physiological processes such as signal transduction and are frequently the targets for the majority of prescription drugs such as beta-blockers and anti-histamines. Understanding the role of sequence variations such as SNPs in GPCRs has potential implications for elucidating disease pathogenesis mechanisms and drug efficacy issues. In

A number of algorithms have been developed to predict the impact of missense mutations on protein structure and function using sequence and structure-based approaches, notably SIFT, PolyPhen-2, Xvar, and Align-GVGD. In this study, Hicks et al. demonstrate that accurately predicting the impact of missense mutations on protein function depends on the algorithm used, the type